Abstract. A randomized, double-blinded, placebo-controlled study was conducted to examine the effect of spatial repellent (SR) in households at risk of malaria in Indonesia. Following presumptive radical cure for malaria in 180 adult men representing sentinels of new infection in four clusters within two villages, all households were given either metofluthrin or placebo mosquito coils. Weekly blood smear screening and human-landing mosquito catches were done throughout the 6 months intervention. Malaria infections occurred in 61 subjects living in placebo households and 31 subjects living in SR coil households, suggesting a 52% protective effect of SR. Likewise, anopheles indoor human landing rates were 32% lower in homes receiving SR coils. Differences in the malaria attack rate between SR-and placebo-treated homes was significant when not accounting for the effects of clustering. When the analysis was adjusted for intra-cluster correlation, the differences between SR-and placebo-treated homes were not statistically significant. The findings provide evidence of SR public health benefit and support a larger trial statistically powered to detect those effects.
A cluster-randomized, double-blinded, placebo-controlled trial was conducted to estimate the protective efficacy (PE) of a spatial repellent (SR) against malaria infection in Sumba, Indonesia. Following radical cure in 1,341 children aged ³ 6 months to £ 5 years in 24 clusters, households were given transfluthrin or placebo passive emanators (devices designed to release vaporized chemical). Monthly blood screening and biweekly human-landing mosquito catches were performed during a 10-month baseline (June 2015-March 2016) and a 24-month intervention period (April 2016-April 2018). Screening detected 164 first-time infections and an accumulative total of 459 infections in 667 subjects in placebo-control households, and 134 first-time and 253 accumulative total infections among 665 subjects in active intervention households. The 24-cluster protective effect of 27.7% and 31.3%, for time to first-event and overall (total new) infections, respectively, was not statistically significant. Purportedly, this was due in part to zero to low incidence in some clusters, undermining the ability to detect a protective effect. Subgroup analysis of 19 clusters where at least one infection occurred during baseline showed 33.3% (P-value = 0.083) and 40.9% (P-value = 0.0236, statistically significant at the onesided 5% significance level) protective effect to first infection and overall infections, respectively. Among 12 moderate-to high-risk clusters, a statistically significant decrease in infection by intervention was detected (60% PE). Primary entomological analysis of impact was inconclusive. Although this study suggests SRs prevent malaria, additional evidence is required to demonstrate the product class provides an operationally feasible and effective means of reducing malaria transmission.
The accuracy of the data class is very important in classification with a machine learning approach. The more accurate the existing data sets and classes, the better the output generated by machine learning. In fact, classification can experience imbalance class data in which each class does not have the same portion of the data set it has. The existence of data imbalance will affect the classification accuracy. One of the easiest ways to correct imbalanced data classes is to balance it. This study aims to explore the problem of data class imbalance in the medium case dataset and to address the imbalance of data classes as well. The Synthetic Minority Over-Sampling Technique (SMOTE) method is used to overcome the problem of class imbalance in obesity status in Indonesia 2013 Basic Health Research (RISKESDAS). The results show that the number of obese class (13.9%) and non-obese class (84.6%). This means that there is an imbalance in the data class with moderate criteria. Moreover, SMOTE with over-sampling 600% can improve the level of minor classes (obesity). As consequence, the classes of obesity status balanced. Therefore, SMOTE technique was better compared to without SMOTE in exploring the obesity status of Indonesia RISKESDAS 2013.
Lingkungan merupakan salah satu faktor penyebab kejadian TB paru. Puskesmas Somba Opu tercatat dari tahun 2009 ditemukan BTA positif sebanyak 84 orang. Mengalami peningkatan pada 2011 tercatat dari sebanyak 116 orang. Penelitian ini bertujuan mengetahui besar risiko kondisi lingkungan terhadap kejadian TB paru di Kabupaten Gowa. Desain penelitian adalah observasional dengan pendekatan case control study. Populasinya adalah penderita TB paru yang tercatat di kartu registrasi pasien di Puskesmas Somba Opu tahun 2011. Sampel penelitian ini adalah penderita TB paru dan tetangga pasien TB paru yang pernah berkunjung ke Puskesmas Somba Opu. Penarikan sampel menggunakan exhaustive sampling dan kelompok kontrol menggunakan purposive sampling dengan besar sampel 130. Perbandingan kasus dengan kontrol 1 : 1. Analisa data yang dilakukan adalah univariat dan bivariat dengan uji odds ratio (OR). Hasil penelitian ini menunjukkan bahwa dari empat variabel yang diteliti rumah yang padat bukan merupakan faktor risiko (OR=0,775), kelembaban rumah yang tidak memenuhi syarat merupakan faktor risiko (OR=2,974), pencahayaan rumah yang tidak memenuhi syarat bukan merupakan faktor risiko (OR=1,070), ventilasi rumah yang kurang bukan merupakan faktor risiko (OR=1,220).
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